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Nuclei Segmentation in Histopathology Images

Abstract: 

Accurate and fast segmentation of nuclei in histopathological images plays a crucial role in cancer research for detection and grading, as well as personal treatment. Despite the important efforts, current algorithms are still suboptimal in terms of speed, adaptivity and generalizability. Popular Deep Convolutional Neural Networks (DCNNs) have recently been utilized for nuclei segmentation, outperforming \textit{traditional} approaches that exploit color and texture features in combination with shallow classifiers or segmentation algorithms. However, DCNNs need large annotated datasets that require extensive amount of time and expert knowledge. In addition, segmentation results obtained by either traditional or DCNN approaches often require a post-processing step to separate cluttered nuclei. In this paper, we propose a computationally efficient nuclei segmentation framework based on DCNNs exhibiting an encoding-decoding structure. We use a partially-annotated dataset and develop an effective training solution. We also use a weighted background model for network to give more importance to borders of nuclei to overcome the problem of clutters. The results of the network demonstrate the lack of necessity for any pre-processing or post-processing step, resulting in a fast and parameter-free system, which presents important advantages with respect to state-of-the-art.

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Paper Details

Authors:
Deniz Mercadier Sayin, Beril Besbinar, Pascal Frossard
Submitted On:
16 May 2019 - 11:13am
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Type:
Presentation Slides
Event:
Presenter's Name:
Beril Besbinar
Paper Code:
3733
Document Year:
2019
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MercadierBesbinarFrossard_ICASSP2019_presentation.pdf

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[1] Deniz Mercadier Sayin, Beril Besbinar, Pascal Frossard, "Nuclei Segmentation in Histopathology Images", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4539. Accessed: May. 23, 2019.
@article{4539-19,
url = {http://sigport.org/4539},
author = {Deniz Mercadier Sayin; Beril Besbinar; Pascal Frossard },
publisher = {IEEE SigPort},
title = {Nuclei Segmentation in Histopathology Images},
year = {2019} }
TY - EJOUR
T1 - Nuclei Segmentation in Histopathology Images
AU - Deniz Mercadier Sayin; Beril Besbinar; Pascal Frossard
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4539
ER -
Deniz Mercadier Sayin, Beril Besbinar, Pascal Frossard. (2019). Nuclei Segmentation in Histopathology Images. IEEE SigPort. http://sigport.org/4539
Deniz Mercadier Sayin, Beril Besbinar, Pascal Frossard, 2019. Nuclei Segmentation in Histopathology Images. Available at: http://sigport.org/4539.
Deniz Mercadier Sayin, Beril Besbinar, Pascal Frossard. (2019). "Nuclei Segmentation in Histopathology Images." Web.
1. Deniz Mercadier Sayin, Beril Besbinar, Pascal Frossard. Nuclei Segmentation in Histopathology Images [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4539